{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import tensorflow as tf\n",
    "import os, re\n",
    "import json\n",
    "import requests\n",
    "import time, datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def leaky_relu(x, alpha=0.1):\n",
    "    x = np.where(x > 0, x, alpha*x)    \n",
    "    return x\n",
    "\n",
    "def normalize(x, x_min, x_max):\n",
    "    x = (2*x - x_max - x_min) / (x_max - x_min + 1e-5)\n",
    "    return x\n",
    "\n",
    "def re_normalize(x, x_min, x_max):\n",
    "    x = ((x_max - x_min + 1e-5) * x + x_max + x_min) / 2\n",
    "    return x\n",
    "\n",
    "\n",
    "def wind_speed_encoding(x):\n",
    "    \"\"\"\n",
    "    风速数值化\n",
    "    \"\"\"\n",
    "    if x == 'AUTO':\n",
    "        return 1\n",
    "    elif x == 'LOW':\n",
    "        return 2\n",
    "    elif x == 'MEDIUM':\n",
    "        return 3\n",
    "    elif x == 'HIGH':\n",
    "        return 4\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def mode_encoding(x):\n",
    "    \"\"\"\n",
    "    模式数值化\n",
    "    \"\"\"\n",
    "    if x == 'AUTO':\n",
    "        return 1\n",
    "    elif x == 'COLD':\n",
    "        return 2\n",
    "    elif x == 'DEHUMI':\n",
    "        return 3\n",
    "    elif x == 'VENT':\n",
    "        return 4\n",
    "    elif x == 'HEAT':\n",
    "        return 5\n",
    "    else:\n",
    "        return 0 \n",
    "    \n",
    "def power_state_encoding(x):\n",
    "    if x == 'ON':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def plot_loss(list_train_loss, list_test_loss):\n",
    "    plt.figure(figsize=(16,6))\n",
    "    plt.plot(list_train_loss, label='train loss')\n",
    "    plt.plot(list_test_loss, label='test loss')\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)    \n",
    "    plt.xlabel('Epoch', fontsize=25)\n",
    "    plt.ylabel('Loss', fontsize=25)\n",
    "    plt.legend(loc='best', fontsize=20)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_acc(list_train_acc, list_test_acc):\n",
    "    plt.figure(figsize=(16,6))\n",
    "    plt.plot(list_train_acc, label='train acc')\n",
    "    plt.plot(list_test_acc, label='test acc')\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)    \n",
    "    plt.xlabel('Epoch', fontsize=25)\n",
    "    plt.ylabel('Accuracy', fontsize=25)\n",
    "    plt.legend(loc='best', fontsize=20)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_model_params():\n",
    "    model_params = {'Network': {'Layers': []}}\n",
    "    return model_params\n",
    "\n",
    "def fc_layer_json(model_params, layer_num, w, b):           \n",
    "    x = model.weights[layer_num].shape.as_list()[0]                     #every input shape\n",
    "    y = 1\n",
    "    z = 1\n",
    "    layer_json = {'LayerType': 'fc', 'InputSize': {'X': x, 'Y': y, 'Z': z},\\\n",
    "                  'OutputSize': {'X': w.shape[1], 'Y': 1, 'Z': 1}, 'Weights': [], 'Biases': {}}\n",
    "    w_data = [w.transpose(1,0).tolist()]\n",
    "    weights_json = {'TDSize': {'X': w.shape[0], 'Y': w.shape[1], 'Z': 1}, 'Data': w_data}\n",
    "    layer_json['Weights'].append(weights_json)\n",
    "    b_data = [b.reshape(-1,1).tolist()]\n",
    "    biases_json = {'TDSize': {'X': 1, 'Y': b.shape[0], 'Z':1}, 'Data': b_data}\n",
    "    layer_json['Biases'] = biases_json\n",
    "    model_params['Network']['Layers'].append(layer_json)    \n",
    "    return model_params\n",
    "\n",
    "def relu_layer_json(model_params, layer_num):\n",
    "    x = model.weights[layer_num].shape.as_list()[1]\n",
    "    y = 1\n",
    "    z = 1    \n",
    "    layer_json = {'LayerType': 'relu', 'InputSize': {'X': x, 'Y': y, 'Z': z}}\n",
    "    model_params['Network']['Layers'].append(layer_json)    \n",
    "    return model_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据导入和预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('transferdata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>dt</th>\n",
       "      <th>power_state</th>\n",
       "      <th>temp_set</th>\n",
       "      <th>mode</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>temp_in</th>\n",
       "      <th>temp_in_start</th>\n",
       "      <th>temp_out</th>\n",
       "      <th>humid_in</th>\n",
       "      <th>humid_in_start</th>\n",
       "      <th>power_state_previous</th>\n",
       "      <th>temp_set_previous</th>\n",
       "      <th>mode_previous</th>\n",
       "      <th>wind_speed_previous</th>\n",
       "      <th>temp_in_delta</th>\n",
       "      <th>temp_in_out</th>\n",
       "      <th>temp_set_in</th>\n",
       "      <th>temp_set_out</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-31 11:30:06</td>\n",
       "      <td>2.233333</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>22.3</td>\n",
       "      <td>21.4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>57.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-31 11:35:52</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>22.3</td>\n",
       "      <td>21.4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>46.9</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-31 11:36:52</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>21.2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>46.9</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-31 11:43:49</td>\n",
       "      <td>15.950000</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>21.2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>46.9</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-31 11:44:51</td>\n",
       "      <td>16.983333</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>20.8</td>\n",
       "      <td>21.4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>40.6</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time         dt  power_state  temp_set  mode  wind_speed  \\\n",
       "0  2019-05-31 11:30:06   2.233333            1        16     2           4   \n",
       "1  2019-05-31 11:35:52   8.000000            1        16     2           4   \n",
       "2  2019-05-31 11:36:52   9.000000            1        16     2           4   \n",
       "3  2019-05-31 11:43:49  15.950000            1        16     2           4   \n",
       "4  2019-05-31 11:44:51  16.983333            1        16     2           4   \n",
       "\n",
       "   temp_in  temp_in_start  temp_out  humid_in  humid_in_start  \\\n",
       "0     22.3           21.4      26.0      57.1            57.0   \n",
       "1     22.3           21.4      26.0      46.9            57.0   \n",
       "2     21.2           21.4      26.0      46.9            57.0   \n",
       "3     21.2           21.4      26.0      46.9            57.0   \n",
       "4     20.8           21.4      26.0      40.6            57.0   \n",
       "\n",
       "   power_state_previous  temp_set_previous  mode_previous  \\\n",
       "0                     0                  0              0   \n",
       "1                     0                  0              0   \n",
       "2                     0                  0              0   \n",
       "3                     0                  0              0   \n",
       "4                     0                  0              0   \n",
       "\n",
       "   wind_speed_previous  temp_in_delta  temp_in_out  temp_set_in  temp_set_out  \n",
       "0                    0            0.9         -4.6         -5.4         -10.0  \n",
       "1                    0            0.9         -4.6         -5.4         -10.0  \n",
       "2                    0           -0.2         -4.6         -5.4         -10.0  \n",
       "3                    0           -0.2         -4.6         -5.4         -10.0  \n",
       "4                    0           -0.6         -4.6         -5.4         -10.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['temp_in_out', 'temp_set_in', 'temp_in_start', 'dt', 'mode', 'wind_speed', 'power_state'] # 变频\n",
    "# features += ['power_state_previous', 'mode_previous'] # 变频+上一次操作\n",
    "x = df[features].values.astype(np.float32)\n",
    "y = df[['temp_in_delta']].values.astype(np.float32)\n",
    "z = df[['temp_in_start']].values.astype(np.float32)\n",
    "shuffled_indexes = np.random.permutation(x.shape[0])\n",
    "x = x[shuffled_indexes]\n",
    "y = y[shuffled_indexes]\n",
    "z = z[shuffled_indexes]\n",
    "x_min, x_max = x.min(axis=0).reshape(1,x.shape[1]), x.max(axis=0).reshape(1,x.shape[1])\n",
    "y_min, y_max = y.min(), y.max()\n",
    "x = normalize(x, x_min, x_max)\n",
    "y = normalize(y, y_min, y_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent = 0.7\n",
    "x_train = x[:int(percent*df.shape[0])]\n",
    "x_test = x[int(percent*df.shape[0]):]\n",
    "y_train = y[:int(percent*df.shape[0])]\n",
    "y_test = y[int(percent*df.shape[0]):]\n",
    "z_train = z[:int(percent*df.shape[0])]\n",
    "z_test = z[int(percent*df.shape[0]):]\n",
    "time_train = df.time[:int(percent*df.shape[0])]\n",
    "time_test = df.time[int(percent*df.shape[0]):]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_epochs = 100\n",
    "initial_learning_rate = 0.001\n",
    "n_classes = 1\n",
    "batch_size = 8\n",
    "n_train = len(x_train)\n",
    "n_test = len(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras方式训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers\n",
    "model = tf.keras.Sequential()\n",
    "model.add(layers.Dense(8, activation='relu', input_shape=(7,)))\n",
    "model.add(layers.Dense(8, activation='relu'))\n",
    "model.add(layers.Dense(8, activation='relu'))\n",
    "model.add(layers.Dense(8, activation='relu'))\n",
    "model.add(layers.Dense(8, activation='relu'))\n",
    "model.add(layers.Dense(n_classes))\n",
    "model.compile(optimizer='adam', loss='mse', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5873 samples, validate on 2518 samples\n",
      "Epoch 1/100\n",
      "5873/5873 [==============================] - 1s 200us/sample - loss: 0.0142 - mae: 0.0824 - val_loss: 0.0102 - val_mae: 0.0712\n",
      "Epoch 2/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0085 - mae: 0.0661 - val_loss: 0.0074 - val_mae: 0.0629\n",
      "Epoch 3/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0076 - mae: 0.0628 - val_loss: 0.0073 - val_mae: 0.0618\n",
      "Epoch 4/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0072 - mae: 0.0615 - val_loss: 0.0066 - val_mae: 0.0583\n",
      "Epoch 5/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0070 - mae: 0.0600 - val_loss: 0.0069 - val_mae: 0.0598\n",
      "Epoch 6/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0069 - mae: 0.0594 - val_loss: 0.0075 - val_mae: 0.0648\n",
      "Epoch 7/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0068 - mae: 0.0591 - val_loss: 0.0062 - val_mae: 0.0571\n",
      "Epoch 8/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0066 - mae: 0.0585 - val_loss: 0.0063 - val_mae: 0.0584\n",
      "Epoch 9/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0066 - mae: 0.0584 - val_loss: 0.0061 - val_mae: 0.0563\n",
      "Epoch 10/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0065 - mae: 0.0582 - val_loss: 0.0065 - val_mae: 0.0590\n",
      "Epoch 11/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0065 - mae: 0.0574 - val_loss: 0.0060 - val_mae: 0.0560\n",
      "Epoch 12/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0065 - mae: 0.0578 - val_loss: 0.0061 - val_mae: 0.0568\n",
      "Epoch 13/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0065 - mae: 0.0575 - val_loss: 0.0061 - val_mae: 0.0566\n",
      "Epoch 14/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0065 - mae: 0.0579 - val_loss: 0.0059 - val_mae: 0.0553\n",
      "Epoch 15/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0064 - mae: 0.0574 - val_loss: 0.0060 - val_mae: 0.0571\n",
      "Epoch 16/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0064 - mae: 0.0577 - val_loss: 0.0059 - val_mae: 0.0562\n",
      "Epoch 17/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0064 - mae: 0.0574 - val_loss: 0.0060 - val_mae: 0.0567\n",
      "Epoch 18/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0063 - mae: 0.0573 - val_loss: 0.0059 - val_mae: 0.0564\n",
      "Epoch 19/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0063 - mae: 0.0573 - val_loss: 0.0060 - val_mae: 0.0569\n",
      "Epoch 20/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0064 - mae: 0.0574 - val_loss: 0.0060 - val_mae: 0.0562\n",
      "Epoch 21/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0063 - mae: 0.0572 - val_loss: 0.0059 - val_mae: 0.0566\n",
      "Epoch 22/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0063 - mae: 0.0570 - val_loss: 0.0061 - val_mae: 0.0563\n",
      "Epoch 23/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0063 - mae: 0.0569 - val_loss: 0.0060 - val_mae: 0.0559\n",
      "Epoch 24/100\n",
      "5873/5873 [==============================] - 1s 115us/sample - loss: 0.0063 - mae: 0.0572 - val_loss: 0.0062 - val_mae: 0.0579\n",
      "Epoch 25/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0063 - mae: 0.0570 - val_loss: 0.0066 - val_mae: 0.0607\n",
      "Epoch 26/100\n",
      "5873/5873 [==============================] - 1s 146us/sample - loss: 0.0063 - mae: 0.0572 - val_loss: 0.0060 - val_mae: 0.0561\n",
      "Epoch 27/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0063 - mae: 0.0568 - val_loss: 0.0058 - val_mae: 0.0554\n",
      "Epoch 28/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0063 - mae: 0.0570 - val_loss: 0.0064 - val_mae: 0.0584\n",
      "Epoch 29/100\n",
      "5873/5873 [==============================] - 1s 112us/sample - loss: 0.0063 - mae: 0.0573 - val_loss: 0.0061 - val_mae: 0.0566\n",
      "Epoch 30/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0062 - mae: 0.0569 - val_loss: 0.0060 - val_mae: 0.0568\n",
      "Epoch 31/100\n",
      "5873/5873 [==============================] - 1s 112us/sample - loss: 0.0062 - mae: 0.0568 - val_loss: 0.0059 - val_mae: 0.0554\n",
      "Epoch 32/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0062 - mae: 0.0568 - val_loss: 0.0058 - val_mae: 0.0557\n",
      "Epoch 33/100\n",
      "5873/5873 [==============================] - 1s 112us/sample - loss: 0.0062 - mae: 0.0569 - val_loss: 0.0066 - val_mae: 0.0606\n",
      "Epoch 34/100\n",
      "5873/5873 [==============================] - 1s 125us/sample - loss: 0.0062 - mae: 0.0567 - val_loss: 0.0072 - val_mae: 0.0628\n",
      "Epoch 35/100\n",
      "5873/5873 [==============================] - 1s 130us/sample - loss: 0.0062 - mae: 0.0569 - val_loss: 0.0057 - val_mae: 0.0556\n",
      "Epoch 36/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0062 - mae: 0.0567 - val_loss: 0.0059 - val_mae: 0.0563\n",
      "Epoch 37/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0062 - mae: 0.0569 - val_loss: 0.0058 - val_mae: 0.0556\n",
      "Epoch 38/100\n",
      "5873/5873 [==============================] - 1s 112us/sample - loss: 0.0062 - mae: 0.0569 - val_loss: 0.0059 - val_mae: 0.0557\n",
      "Epoch 39/100\n",
      "5873/5873 [==============================] - 1s 114us/sample - loss: 0.0062 - mae: 0.0564 - val_loss: 0.0065 - val_mae: 0.0593\n",
      "Epoch 40/100\n",
      "5873/5873 [==============================] - 1s 112us/sample - loss: 0.0062 - mae: 0.0565 - val_loss: 0.0059 - val_mae: 0.0567\n",
      "Epoch 41/100\n",
      "5873/5873 [==============================] - 1s 112us/sample - loss: 0.0061 - mae: 0.0564 - val_loss: 0.0059 - val_mae: 0.0569\n",
      "Epoch 42/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0061 - mae: 0.0565 - val_loss: 0.0058 - val_mae: 0.0554\n",
      "Epoch 43/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0061 - mae: 0.0567 - val_loss: 0.0059 - val_mae: 0.0559\n",
      "Epoch 44/100\n",
      "5873/5873 [==============================] - 1s 117us/sample - loss: 0.0061 - mae: 0.0563 - val_loss: 0.0060 - val_mae: 0.0561\n",
      "Epoch 45/100\n",
      "5873/5873 [==============================] - 1s 122us/sample - loss: 0.0062 - mae: 0.0568 - val_loss: 0.0058 - val_mae: 0.0555\n",
      "Epoch 46/100\n",
      "5873/5873 [==============================] - 1s 132us/sample - loss: 0.0061 - mae: 0.0564 - val_loss: 0.0057 - val_mae: 0.0550\n",
      "Epoch 47/100\n",
      "5873/5873 [==============================] - 1s 125us/sample - loss: 0.0061 - mae: 0.0565 - val_loss: 0.0059 - val_mae: 0.0559\n",
      "Epoch 48/100\n",
      "5873/5873 [==============================] - 1s 118us/sample - loss: 0.0062 - mae: 0.0565 - val_loss: 0.0062 - val_mae: 0.0576\n",
      "Epoch 49/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0060 - mae: 0.0561 - val_loss: 0.0061 - val_mae: 0.0572\n",
      "Epoch 50/100\n",
      "5873/5873 [==============================] - 1s 113us/sample - loss: 0.0061 - mae: 0.0565 - val_loss: 0.0059 - val_mae: 0.0562\n",
      "Epoch 51/100\n",
      "5873/5873 [==============================] - 1s 116us/sample - loss: 0.0061 - mae: 0.0563 - val_loss: 0.0058 - val_mae: 0.0555\n",
      "Epoch 52/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0061 - mae: 0.0564 - val_loss: 0.0065 - val_mae: 0.0596\n",
      "Epoch 53/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0060 - mae: 0.0563 - val_loss: 0.0061 - val_mae: 0.0567\n",
      "Epoch 54/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0060 - mae: 0.0563 - val_loss: 0.0058 - val_mae: 0.0553\n",
      "Epoch 55/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0061 - mae: 0.0563 - val_loss: 0.0059 - val_mae: 0.0562\n",
      "Epoch 56/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0061 - mae: 0.0561 - val_loss: 0.0058 - val_mae: 0.0558\n",
      "Epoch 57/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0060 - mae: 0.0561 - val_loss: 0.0058 - val_mae: 0.0551\n",
      "Epoch 58/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0060 - mae: 0.0561 - val_loss: 0.0061 - val_mae: 0.0568\n",
      "Epoch 59/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0060 - mae: 0.0559 - val_loss: 0.0061 - val_mae: 0.0571\n",
      "Epoch 60/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0060 - mae: 0.0563 - val_loss: 0.0063 - val_mae: 0.0585\n",
      "Epoch 61/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0060 - mae: 0.0563 - val_loss: 0.0060 - val_mae: 0.0561\n",
      "Epoch 62/100\n",
      "5873/5873 [==============================] - 1s 112us/sample - loss: 0.0060 - mae: 0.0562 - val_loss: 0.0059 - val_mae: 0.0563\n",
      "Epoch 63/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0060 - mae: 0.0562 - val_loss: 0.0059 - val_mae: 0.0557\n",
      "Epoch 64/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0060 - mae: 0.0562 - val_loss: 0.0060 - val_mae: 0.0565\n",
      "Epoch 65/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0061 - mae: 0.0565 - val_loss: 0.0061 - val_mae: 0.0569\n",
      "Epoch 66/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0060 - mae: 0.0564 - val_loss: 0.0059 - val_mae: 0.0564\n",
      "Epoch 67/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0060 - mae: 0.0565 - val_loss: 0.0058 - val_mae: 0.0558\n",
      "Epoch 68/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0060 - mae: 0.0561 - val_loss: 0.0062 - val_mae: 0.0582\n",
      "Epoch 69/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0060 - mae: 0.0561 - val_loss: 0.0059 - val_mae: 0.0560\n",
      "Epoch 70/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0059 - mae: 0.0558 - val_loss: 0.0060 - val_mae: 0.0578\n",
      "Epoch 71/100\n",
      "5873/5873 [==============================] - 1s 116us/sample - loss: 0.0060 - mae: 0.0563 - val_loss: 0.0059 - val_mae: 0.0566\n",
      "Epoch 72/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0059 - mae: 0.0561 - val_loss: 0.0062 - val_mae: 0.0572\n",
      "Epoch 73/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0059 - mae: 0.0559 - val_loss: 0.0058 - val_mae: 0.0558\n",
      "Epoch 74/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0059 - mae: 0.0560 - val_loss: 0.0059 - val_mae: 0.0560\n",
      "Epoch 75/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0059 - mae: 0.0558 - val_loss: 0.0057 - val_mae: 0.0553\n",
      "Epoch 76/100\n",
      "5873/5873 [==============================] - 1s 111us/sample - loss: 0.0059 - mae: 0.0560 - val_loss: 0.0059 - val_mae: 0.0560\n",
      "Epoch 77/100\n",
      "5873/5873 [==============================] - 1s 112us/sample - loss: 0.0059 - mae: 0.0557 - val_loss: 0.0060 - val_mae: 0.0561\n",
      "Epoch 78/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0059 - mae: 0.0557 - val_loss: 0.0058 - val_mae: 0.0557\n",
      "Epoch 79/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0059 - mae: 0.0561 - val_loss: 0.0061 - val_mae: 0.0569\n",
      "Epoch 80/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0059 - mae: 0.0557 - val_loss: 0.0057 - val_mae: 0.0550\n",
      "Epoch 81/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0059 - mae: 0.0557 - val_loss: 0.0057 - val_mae: 0.0556\n",
      "Epoch 82/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0058 - mae: 0.0555 - val_loss: 0.0058 - val_mae: 0.0554\n",
      "Epoch 83/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0058 - mae: 0.0556 - val_loss: 0.0059 - val_mae: 0.0565\n",
      "Epoch 84/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0059 - mae: 0.0560 - val_loss: 0.0061 - val_mae: 0.0573\n",
      "Epoch 85/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0059 - mae: 0.0556 - val_loss: 0.0058 - val_mae: 0.0554\n",
      "Epoch 86/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0059 - mae: 0.0557 - val_loss: 0.0059 - val_mae: 0.0564\n",
      "Epoch 87/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0059 - mae: 0.0557 - val_loss: 0.0059 - val_mae: 0.0561\n",
      "Epoch 88/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0058 - mae: 0.0553 - val_loss: 0.0059 - val_mae: 0.0563\n",
      "Epoch 89/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0058 - mae: 0.0554 - val_loss: 0.0063 - val_mae: 0.0585\n",
      "Epoch 90/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0058 - mae: 0.0555 - val_loss: 0.0064 - val_mae: 0.0590\n",
      "Epoch 91/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0058 - mae: 0.0554 - val_loss: 0.0061 - val_mae: 0.0564\n",
      "Epoch 92/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0058 - mae: 0.0553 - val_loss: 0.0057 - val_mae: 0.0547\n",
      "Epoch 93/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0058 - mae: 0.0554 - val_loss: 0.0060 - val_mae: 0.0566\n",
      "Epoch 94/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0059 - mae: 0.0555 - val_loss: 0.0059 - val_mae: 0.0555\n",
      "Epoch 95/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0058 - mae: 0.0551 - val_loss: 0.0058 - val_mae: 0.0559\n",
      "Epoch 96/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0058 - mae: 0.0554 - val_loss: 0.0056 - val_mae: 0.0544\n",
      "Epoch 97/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0058 - mae: 0.0549 - val_loss: 0.0058 - val_mae: 0.0551\n",
      "Epoch 98/100\n",
      "5873/5873 [==============================] - 1s 121us/sample - loss: 0.0059 - mae: 0.0557 - val_loss: 0.0060 - val_mae: 0.0567\n",
      "Epoch 99/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0058 - mae: 0.0553 - val_loss: 0.0059 - val_mae: 0.0566\n",
      "Epoch 100/100\n",
      "5873/5873 [==============================] - 1s 120us/sample - loss: 0.0058 - mae: 0.0551 - val_loss: 0.0057 - val_mae: 0.0550\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=n_epochs, batch_size=batch_size, \\\n",
    "                    validation_data=(x_test,y_test), verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 8)                 64        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 8)                 72        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 8)                 72        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 8)                 72        \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 8)                 72        \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 361\n",
      "Trainable params: 361\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Exclude the first few epochs so the graph is easier to read\n",
    "SKIP = 10\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n",
    "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "# Draw a graph of mean absolute error, which is another way of\n",
    "# measuring the amount of error in the prediction.\n",
    "mae = history.history['mae']\n",
    "val_mae = history.history['val_mae']\n",
    "\n",
    "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n",
    "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n",
    "plt.title('Training and validation mean absolute error')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MAE')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = model.evaluate(x_test, y_test, verbose=False)\n",
    "predictions = model.predict(x_test)\n",
    "\n",
    "for i in range(len(features)):\n",
    "    plt.clf()\n",
    "    plt.title('Comparison of predictions and actual values')\n",
    "    plt.plot(x_test[:,i], y_test.reshape(len(y_test)), 'b.', label='Actual')\n",
    "    plt.plot(x_test[:,i], predictions.reshape(len(predictions)), 'r.', label='Predicted')\n",
    "    plt.xlabel(features[i])\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tensorflow方式训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_generator(batch_size):\n",
    "    batch = 0\n",
    "    while batch < n_train // batch_size:\n",
    "        indexes = range(batch * batch_size, (batch + 1) * batch_size)\n",
    "        batch_x_train = x_train[indexes]\n",
    "        batch_y_train = y_train[indexes]\n",
    "        batch += 1\n",
    "        yield (batch_x_train, batch_y_train)\n",
    "        \n",
    "    return\n",
    "\n",
    "def test_generator(batch_size):\n",
    "    batch = 0\n",
    "    while batch < n_test // batch_size:\n",
    "        indexes = range(batch * batch_size, (batch + 1) * batch_size)\n",
    "        batch_x_test = x_test[indexes]\n",
    "        batch_y_test = y_test[indexes]\n",
    "        batch += 1\n",
    "        yield (batch_x_test, batch_y_test)\n",
    "        \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense, Flatten\n",
    "from tensorflow.keras import Model\n",
    "\n",
    "class TempPredModel(Model):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.d1 = Dense(8, activation='relu')\n",
    "        self.d2 = Dense(8, activation='relu')\n",
    "        self.d3 = Dense(8, activation='relu')\n",
    "        self.d4 = Dense(8, activation='relu')\n",
    "        self.d5 = Dense(8, activation='relu')\n",
    "        self.d6 = Dense(n_classes, activation=None)\n",
    "\n",
    "    @tf.function\n",
    "    def call(self, x):\n",
    "        x = self.d1(x)\n",
    "        x = self.d2(x)\n",
    "        x = self.d3(x)\n",
    "        x = self.d4(x)\n",
    "        x = self.d5(x)\n",
    "        x = self.d6(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 需要一起更新，loss才能更新，略奇怪？\n",
    "\n",
    "model = TempPredModel()\n",
    "\n",
    "loss_object = tf.keras.losses.MeanSquaredError()\n",
    "optimizer = tf.keras.optimizers.Adam()\n",
    "\n",
    "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
    "train_mae = tf.keras.metrics.MeanAbsoluteError(name='train_mae')\n",
    "\n",
    "test_loss = tf.keras.metrics.Mean(name='test_loss')\n",
    "test_mae = tf.keras.metrics.MeanAbsoluteError(name='test_mae')\n",
    "\n",
    "@tf.function\n",
    "def train_step(x, y):\n",
    "    with tf.GradientTape() as tape:\n",
    "        predictions = model(x)\n",
    "        loss = loss_object(y, predictions)\n",
    "    gradients = tape.gradient(loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "    train_loss(loss)\n",
    "    train_mae(y, predictions)\n",
    "    \n",
    "@tf.function\n",
    "def test_step(x, y):\n",
    "    predictions = model(x)\n",
    "    loss = loss_object(y, predictions)\n",
    "\n",
    "    test_loss(loss)\n",
    "    test_mae(y, predictions)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loss_temp = 1e10\n",
    "test_mae_temp = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100: Train Loss: 0.013, Test Loss: 0.009, Train Mae: 7.601, Test Mae: 6.516\n",
      "Epoch 10/100: Train Loss: 0.008, Test Loss: 0.007, Train Mae: 6.282, Test Mae: 6.041\n",
      "Epoch 20/100: Train Loss: 0.007, Test Loss: 0.007, Train Mae: 6.121, Test Mae: 5.928\n",
      "Epoch 30/100: Train Loss: 0.007, Test Loss: 0.007, Train Mae: 6.034, Test Mae: 5.868\n",
      "Epoch 40/100: Train Loss: 0.007, Test Loss: 0.007, Train Mae: 5.976, Test Mae: 5.826\n",
      "Epoch 50/100: Train Loss: 0.007, Test Loss: 0.006, Train Mae: 5.931, Test Mae: 5.797\n",
      "Epoch 60/100: Train Loss: 0.007, Test Loss: 0.006, Train Mae: 5.897, Test Mae: 5.780\n",
      "Epoch 70/100: Train Loss: 0.007, Test Loss: 0.006, Train Mae: 5.869, Test Mae: 5.771\n",
      "Epoch 80/100: Train Loss: 0.007, Test Loss: 0.006, Train Mae: 5.845, Test Mae: 5.766\n",
      "Epoch 90/100: Train Loss: 0.007, Test Loss: 0.006, Train Mae: 5.824, Test Mae: 5.760\n",
      "Epoch 100/100: Train Loss: 0.007, Test Loss: 0.006, Train Mae: 5.806, Test Mae: 5.751\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(n_epochs):\n",
    "    for batch_x_train, batch_y_train in train_generator(batch_size):\n",
    "        train_step(batch_x_train, batch_y_train)\n",
    "        \n",
    "    for batch_x_test, batch_y_test in test_generator(batch_size):\n",
    "        test_step(batch_x_test, batch_y_test)\n",
    "\n",
    "    logger = 'Epoch {}/{}: Train Loss: {:.3f}, Test Loss: {:.3f}, Train Mae: {:.3f}, Test Mae: {:.3f}'\n",
    "    if (epoch+1) % 10 == 0 or (epoch+1) == 1:\n",
    "        print(logger.format(epoch+1, n_epochs,\n",
    "                            train_loss.result(),\n",
    "                            test_loss.result(),\n",
    "                            train_mae.result()*100,\n",
    "                            test_mae.result()*100))\n",
    "    if test_loss.result() < test_loss_temp:\n",
    "        test_loss_temp = test_loss.result()\n",
    "        test_mae_temp =  test_mae.result()*100\n",
    "        layer_num = 0 \n",
    "        w1, b1 = model.weights[layer_num].numpy(),model.weights[layer_num+1].numpy().astype(np.float64)\n",
    "        layer_num += 2\n",
    "        w2, b2 = model.weights[layer_num].numpy(),model.weights[layer_num+1].numpy().astype(np.float64)\n",
    "        layer_num += 2\n",
    "        w3, b3 = model.weights[layer_num].numpy(),model.weights[layer_num+1].numpy().astype(np.float64)\n",
    "        layer_num += 2\n",
    "        w4, b4 = model.weights[layer_num].numpy(),model.weights[layer_num+1].numpy().astype(np.float64)\n",
    "        layer_num += 2\n",
    "        w5, b5 = model.weights[layer_num].numpy(),model.weights[layer_num+1].numpy().astype(np.float64)\n",
    "        layer_num += 2\n",
    "        w6, b6 = model.weights[layer_num].numpy(),model.weights[layer_num+1].numpy().astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_num = 0                                                          #model.weights[layer_num]\n",
    "model_params = init_model_params()\n",
    "model_params = fc_layer_json(model_params, layer_num, w1, b1)\n",
    "model_params = relu_layer_json(model_params, layer_num)\n",
    "layer_num += 2\n",
    "model_params = fc_layer_json(model_params, layer_num, w1, b1)\n",
    "model_params = relu_layer_json(model_params, layer_num)\n",
    "layer_num += 2\n",
    "model_params = fc_layer_json(model_params, layer_num, w1, b1)\n",
    "model_params = relu_layer_json(model_params, layer_num)\n",
    "layer_num += 2\n",
    "model_params = fc_layer_json(model_params, layer_num, w1, b1)\n",
    "model_params = relu_layer_json(model_params, layer_num)\n",
    "layer_num += 2\n",
    "model_params = fc_layer_json(model_params, layer_num, w1, b1)\n",
    "model_params = relu_layer_json(model_params, layer_num)\n",
    "layer_num += 2\n",
    "model_params = fc_layer_json(model_params, layer_num, w1, b1)\n",
    "model_params = relu_layer_json(model_params, layer_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = {'scale': {'xmin':list(np.float64(x_min.tolist()[0])), 'xmax':list(np.float64(x_max.tolist()[0])), 'ymin':list(np.float64([y_min])), 'ymax':list(np.float64([y_max]))}}\n",
    "transfermodel = scale.copy()\n",
    "transfermodel.update({'modelparams': model_params})\n",
    "with open('transfermodel.txt', 'w') as f:\n",
    "    content = json.dumps(transfermodel)\n",
    "    f.write(content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 保存并转换模型为c代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd ../edge/src/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存非量化模型\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n",
    "open(\"transfer_model.tflite\", \"wb\").write(tflite_model)\n",
    "\n",
    "# 保存量化模型\n",
    "# converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "# converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n",
    "# tflite_model = converter.convert()\n",
    "# open(\"transfer_model_quantized.tflite\", \"wb\").write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型转换为c代码\n",
    "!xxd -i transfer_model.tflite > transfer_model.cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存最值到c代码\n",
    "with open('transfer_model.cc', 'a') as f:\n",
    "    f.write('const float x_min[7] = {};\\n'.format(str([round(i, 3) for i in x_min.tolist()[0]]).replace('[','{').replace(']','}')))\n",
    "    f.write('const float x_max[7] = {};\\n'.format(str([round(i, 3) for i in x_max.tolist()[0]]).replace('[','{').replace(']','}')))\n",
    "    f.write('const float y_min = {:.3f};\\n'.format(y_min))\n",
    "    f.write('const float y_max = {:.3f};'.format(y_max))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对比python和c的预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_x_test[1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model(batch_x_test[1:2]).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!./transfermodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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